Gender: Male 
Age: 25-34
Nationality: United States
Ethnicity: White / Caucasian;Other. Please specify Cajun
Marital status: Single 
First language: English 
Religion: Nothing 
Education: High school or below
Mother's education: High school or below
Father's education: High school or below
Self-assessed socioeconomic status (1-10): 2
Home: Other (Please specify) Live with family
Employment: Self-employed part time
Occupation: Professionals or highly skilled workers (those who increase the existing stock of knowledge or teach about it in a systematic manner, e.g. engineer, doctor, teacher, lawyer)
Mother's occupation: Elementary occupations (jobs that involve simple and routine tasks which may require the use of hand-held tools and considerable physical effort)
Father's occupation: Craft related trades workers (those who produce or process foodstuffs, textiles, wooden, metal and other articles, including handicraft goods, and apply specific technical and practical knowledge and skills to construct and maintain buildings)
Hobbies: Exercise;Use social media;Listen to rock/indie music;Listen to hiphop and rap 
Devices used daily: Laptop;Smartphone 
NLP tools known: Spell checker (i.e., correcting misspellings);Grammar checker (i.e., correcting grammar mistakes);E-mail spam detection (i.e., automatically send spam e-mails to your Trash folder);Sentiment analysis (e.g. automatically detect if user reviews are positive or negative);Machine translation (e.g. translate Chinese or Russian into English, and vice versa);Paraphrasing and Summarisation (i.e., automatically produce alternative descriptions of written text);Question Answering & Search Engine. (e.g., browsers or ask questions to smart devices/assistants);Dialog technology (e.g. chatbots that communicate with you such as chatGPT);Speech-to-Text (i.e. a computer transcribing your spoken language to written language);Text-to-Speech (i.e, computers being able to read out loud written language);Reading text from scanned documents (i.e. “Optical character recognition” or OCR. Given an image representing printed text, determine the corresponding text.) 
NLP tools used: Spell checker (i.e., correcting misspellings);E-mail spam detection (i.e., automatically send spam e-mails to your Trash folder);Grammar checker (i.e., correcting grammar mistakes);Sentiment analysis (e.g. automatically detect if user reviews are positive or negative);Machine translation (e.g. translate Chinese or Russian into English, and vice versa);Paraphrasing and Summarisation (i.e., automatically produce alternative descriptions of written text);Question Answering & Search Engine. (e.g., browsers or ask questions to smart devices/assistants);Dialog technology (e.g. chatbots that communicate with you such as chatGPT);Speech-to-Text (i.e. a computer transcribing your spoken language to written language);Text-to-Speech (i.e, computers being able to read out loud written language);Reading text from scanned documents (i.e. “Optical character recognition” or OCR. Given an image representing printed text, determine the corresponding text.) 
NLP tools they would find useful: Spell checker (i.e., correcting misspellings) 
Frequency of LLM use: Every day
LLMs used: ChatGPT;Grok;Snapchat My AI;Microsoft Bing AI;GitHub Copilot;Claude 
LLM use cases: Coding;Translation;Brainstorming;Writing (e.g., composing emails);Paraphrasing or finding synonyms;Proofreading/Editing (e.g., grammar, spelling, structure improvements);Digital content creation (e.g., creation of social media content);Generic chatbot conversation;Solving math or logical problems;Analyze data;Generate art;Play games;Completing assignments;Learning (e.g., explaining complex concepts in simple terms);Get information about current events;Answering questions about general knowledge;Collecting references;Summarizing long texts 
Contexts of LLM use: School/University;Work;Entertainment;Learning;Personal;Creative or artistic;Technical 
Prompt 1:  translate into high school English. Try to preserve nuance, but prioritize readability: you do have leniency in making the text sound as natural as possible, like fluent English. Gegentypus = antitype, gegentypisch = antitypish, volk = people, voelkish = racial. Do NOT introduce new headers or formatting or bold or bullets, etc. Preserve quotes.
Prompt 2: analyze the document to help me identify and recognize scum tells. i.e. things players will say in chat that strongly indicate they are scum, or that strongly suggest their innocence. include verbatim quotes to support ur analysis. then, list every possible role, and identifya  few DOs and DONTs for that role
Prompt 3: Based on these records of ranked Town of Salem game transcripts, what do you think is optimal strategy for Mafia?
Prompt 4: translate the following text into high school level English. Prioritize comprehensibility over faithfulness to nuance, but try to preserve both. Do NOT bold shit. Do NOT create ur own subtitles. Do NOT create ur own custom formratting. Do NOT EVER paraphrarse quotes.  Die Intelligenzprüfung unter völkischem und typologischem Gesichtspunkt Ein Beitrag zum Problem der Auslese  Von FRIEDRICH BECKER (Marburg) Mit 147 Abbildungen  Aufgabe und Ziel Die Erforschung der Intelligenz ist einer der jüngsten Zweige der Psychologie. Es sind etwa 40 Jahre her, daß dieses Problem in der psychologischen Forschung auftauchte, und seitdem bemüht man sich um brauchbare Methoden zur quantitativen und qualitativen Feststellung der Intelligenz.  Von den verschiedensten Seiten her suchte man einen Weg zur Lösung dieses Problems zu finden. Die Methoden, die man anwandte, waren außerordentlich vielgestaltig. Gemeinsam war ihnen allen nur das eine: Sie behandelten die Menschen, die geprüft und untersucht wurden, wie eine qualitativ gleichgeartete, homogene Masse.  Diese Auffassung entsprang der unorganischen und materialistischen Denkweise der „verklingenden Kulturepoche“. Es blieb unberücksichtigt, daß man keine mechanisch reagierenden Maschinen vor sich hatte, sondern lebendige Menschen, die nicht nur einen quantitativen Unterschied im Intelligenzgrad aufwiesen, sondern infolge ihrer verschiedenen Persönlichkeitsstruktur auf die einzelnen Versuchsreihen auch qualitativ verschieden reagieren mußten.  Ein zweiter Irrtum war die Überschätzung des sog. „reinen Intellekts“, d. h. einer besonderen Form des Intellekts. Dieser reine Intellekt hatte bis in die jüngste Zeit eine Herrschaftsstellung auf fast allen Betätigungsgebieten des menschlichen Geistes. Er betätigte sich einerseits im Materialismus und Positivismus und anderseits im philosophischen Idealismus, den beiden kulturellen Hauptmächten der jüngsten Vergangenheit,  Besonders verhängnisvoll aber hat sich auf dem Gebiete der Intelligenzforschung die Vorherrschaft der jüdischen Psychologie ausgewirkt. Diese nahm — zumindest ihrem äußeren Einfluß nach — hier geradezu eine Vormachtstellung ein.  Unsere Untersuchung, die das Problem der Prüfungsmethoden zum Gegenstand hat, will zunächst einmal in großen Linien zeigen, wo die Hauptschäden liegen und welche Nachteile sich für unser Volk ergeben, wenn die Ausleseverfahren nach den, bisher üblichen Prüfungsmethoden weiterhin gehandhabt werden. Vorliegende Arbeit ist eine Kritik der Intelligenzprüfungsmethoden auf der experimentellen Grundlage der Integrationstypologie von E.R. Jaensch.  Durchgeführt wurden die Untersuchungen von 1933—1936 in Marburg und Wetzlar an einem Material von insgesamt 312 Vpn., im Alter von 14 bis 35 Jahren. Sie setzten sich aus den verschiedensten Berufsständen zusammen: 151 Gewerbeschüler aus folgenden Beurfen: Mechaniker 85; Schlosser, Maurer je 5; Klempner, Austreicher, Gärtner, Schuhmacher je 4; Schmiede, Buchdrucker, Sattler, Frisöre, Bäcker je 3; Autoschlosser, Elektriker, Zimmerer, Schreiner, Schneider, Bürogehilfen je 2; Pflasterer, Metzger, Töpfer, Steinmetz, Musiker, Hausdiener j1, berufslos 7. Oberrealschüler und Realgymnasiasten aus den Klassen OIII bis OI, insbesondere aus OII und OI 65; Mechaniker (teilweise mit mehrjähriger Auslandspraxis als Feinmechaniker und Vertreter) 60; Studentinnen, Studenten und Erzieher 36.
Prompt 5: library(jsonlite) library(ggplot2) library(dplyr) library(tidyr)  # Load JSON data json_file <- "data.json" data <- fromJSON(json_file)  # Convert JSON data to data frame data_df <- bind_rows(data, .id = "Role") %>%   mutate(Total = Town + Scum,          Town_Percent = (Town / Total) * 100,          Scum_Percent = (Scum / Total) * 100,          Town_CI = 1.96 * sqrt((Town_Percent / 100) * (1 - Town_Percent / 100) / Total) * 100,          Scum_CI = 1.96 * sqrt((Scum_Percent / 100) * (1 - Scum_Percent / 100) / Total) * 100)  # Reshape data for plotting plot_data <- data_df %>%   select(Role, Town_Percent, Scum_Percent, Town_CI, Scum_CI, Total) %>%   pivot_longer(cols = c(Town_Percent, Scum_Percent), names_to = "Group", values_to = "Percentage") %>%   mutate(CI = if_else(Group == "Town_Percent", Town_CI, Scum_CI),          Group = recode(Group, Town_Percent = "Town", Scum_Percent = "Scum"),          Role = paste0(Role, "\nn = ", Total)) %>%   arrange(desc(Total), Group == "Scum")  # Plot the bar graph p <- ggplot(plot_data, aes(x = reorder(Role, Total), y = Percentage, fill = Group)) +   geom_bar(stat = "identity", position = position_dodge(width = 0.6), width = 0.5) +   geom_errorbar(aes(ymin = pmax(0, Percentage - CI), ymax = Percentage + CI),                 position = position_dodge(width = 0.6), width = 0.2) +   scale_y_continuous(breaks = seq(0, 100, by = 5), labels = scales::percent_format(scale = 1)) +   scale_fill_manual(values = c("Town" = "green", "Scum" = "red")) +   labs(     title = "Placeholder Title",     subtitle = "Placeholder Subtitle",     x = "Role Claim",     y = "Percentage",     fill = "Legend"   ) +   theme_minimal() +   theme(     plot.title = element_text(hjust = 0.5),     plot.subtitle = element_text(hjust = 0.5),     axis.text.x = element_text(angle = 45, hjust = 1)   )  # Save the plot to a PDF ggsave("data.pdf", plot = p, width = 8, height = 6)   # Rscript r-data.r  fix this code so leftmost pair of bars is pair with highest town percent...  also in each pair, i want town bar on left  dont give me the whole code. only give me the snippets i need to replace
Prompt 6: how to make footer in flex body hug bottom of page without margin-top: auto
Prompt 7: r # Load required libraries library(jsonlite) library(ggplot2) options(width = 9999)  # Adjust console width  # USER SETTINGS # Specify p-value method: "bonferroni" or "regular" p_value_method <- "bonferroni"  # Load the JSON data from file file_path <- "deaths-roles-all.json"  # Replace with your actual file path data <- fromJSON(file_path)  # Convert JSON data into a data frame df <- do.call(rbind, lapply(data, as.data.frame)) df <- as.data.frame(df)  # Ensure the columns are numeric where required df$n <- as.numeric(as.character(df$n)) df$nDead <- as.numeric(as.character(df$nDead))  # Calculate probabilities and convert to percentages df$percent <- round((df$nDead / df$n) * 100, 4)  # Perform a one-sample proportion test for each role overall_prob <- sum(df$nDead) / sum(df$n) overall_percent <- round(overall_prob * 100, 4)  # Overall percentage  # Function to calculate critical range for effect size get_critical_range <- function(n, overall_prob, alpha = 0.05, n_roles) {   # Bonferroni-corrected alpha   adjusted_alpha <- alpha / n_roles   # Standard error for proportions   se <- sqrt(overall_prob * (1 - overall_prob) / n)   # Critical value for effect size (z-score for two-tailed test)   z <- qnorm(1 - adjusted_alpha / 2)   margin <- z * se   lower_bound <- max(0, overall_prob - margin) # Clip at 0   upper_bound <- overall_prob + margin   return(round(c(lower_bound, upper_bound) * 100, 4))  # Convert to percentages }  # Calculate p-values, critical range, and expected death rate df$p_value <- sapply(1:nrow(df), function(i) {   prop.test(df$nDead[i], df$n[i], p = overall_prob)$p.value })  n_roles <- nrow(df) df$critical_range <- t(sapply(df$n, function(n) get_critical_range(n, overall_prob, n_roles = n_roles))) df$expected_percent <- round(overall_prob * 100, 4)  # Calculate critical effect size df$critical_effect_size <- sapply(1:nrow(df), function(i) {   if (df$percent[i] < df$critical_range[i, 1]) {     return(round(df$critical_range[i, 1] - df$percent[i], 4))   } else if (df$percent[i] > df$critical_range[i, 2]) {     return(round(df$percent[i] - df$critical_range[i, 2], 4))   } else {     return(0)   } })  # Round p-values df$p_value <- round(df$p_value, 4)  # Apply Bonferroni correction df$bonferroni_p <- pmin(round(df$p_value * n_roles, 4), 1)  # Cap at 1  # Select which p-value method to use df$final_p_value <- ifelse(p_value_method == "bonferroni", df$bonferroni_p, df$p_value)  # Categorize risk df$risk_category <- ifelse(df$final_p_value < 0.05 & df$percent > overall_percent, "high",                            ifelse(df$final_p_value < 0.05 & df$percent < overall_percent, "low", "null"))  # Calculate effect size (difference in percentage points) df$effect_size <- round(df$percent - overall_percent, 4)  # Set options to avoid scientific notation options(scipen = 999)  # Create a custom factor for risk_category to control sorting order df$risk_category <- factor(df$risk_category, levels = c("high", "null", "low"))  # Sort the data frame: by percent likelihood of death df <- df[order(-df$percent), ]  # Print overall expected percentage cat("Overall Expected Percent:", overall_percent, "%\n")  # Display results print(df[, c("n", "nDead", "expected_percent", "percent", "effect_size", "critical_range", "critical_effect_size", "final_p_value", "risk_category")])  # Define colors based on roles role_colors <- c(   "Random" = "#1964C7",   "Any" = "#49A9D0",   "Town" = "#45BF00",   "Mafia" = "#DD0000",   "Mafioso" = "#DD0000",   "Godfather" = "#DD0000",   "Executioner" = "#ACACAC",   "Neutral" = "#ACACAC",   "Jester" = "#F49FD0",   "Witch" = "#BF5FFF",   "Serial Killer" = "#336eff",   "Werewolf" = "#744A26",   "Arsonist" = "#EE7600",   "Vampire" = "#7B8867",   "Survivor" = "#C8C800",   "Amnesiac" = "#4FF0E8",   "Coven" = "#BF5FFF",   "Coven Leader" = "#BF5FFF", "Hex Master" = "#BF5FFF", "Medusa" = "#BF5FFF", "Necromancer" = "#BF5FFF", "Poisoner" = "#BF5FFF", "Potion Master" = "#BF5FFF",   "Pirate" = "#EDC240",   "Plaguebearer" = "#cfff63",   "Pestilence" = "#cfff63",   "Guardian Angel" = "#FFFFFF",   "Juggernaut" = "#631A35" )  # Set default color to "Town" color default_color <- "#45BF00" df$fill_color <- ifelse(df$role %in% names(role_colors), role_colors[df$role], default_color)  # PLOT RESULTS output_file <- "death_probabilities_plot.pdf" pdf(output_file, width = 10, height = 8)  # Create bar plot ggplot(df, aes(x = reorder(row.names(df), -percent), y = percent, fill = fill_color)) +   geom_bar(stat = "identity", color = NA, size = 0.5) +   geom_errorbar(aes(ymin = pmax(0, critical_range[, 1]), ymax = critical_range[, 2]), width = 0.2) +   geom_hline(yintercept = overall_percent, linetype = "dotted", color = "black") +   scale_y_continuous(breaks = seq(0, 100, by = 5)) +   labs(title = "Death Probability by Role",        x = "Role",        y = "Percentage Likelihood of Death (%)") +   scale_fill_identity() +   theme(axis.text.x = element_text(angle = 45, hjust = 1))  dev.off()  cat("Plot saved to:", output_file, "\n")  # Rscript r3.r  add bonferroni-corrected 95% confidence interval to each bar. clip them at zero so they dont go below the graph
Prompt 8: how to say correctly "quadrupledly married"
Prompt 9: what is r/bratlife
Prompt 10: all demographic data on this individual with verbatim quotes to support claims. ALL demographic data.
Comments: some of my prompts involve an uploaded document so they don't make sense out of context